Robust semiparametric M-estimation and the weighted bootstrap

By Shuangge Ma, Michael R. Kosorok. 2005

136 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d9a9e1711ec29b662a54c6b3e71708f4397ccbb3

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In Statistics and Probability

Citations in the corpus

Discrete games with flexible information structures: an application to local grocery markets (2014, methodology): “The weighted bootstrap was introduced by Ma and Kosorok (2005) for use in attaining confidence intervals for the finite dimensional parameters in a semi-parametric estimation of a point–identified problem.”
Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes (2016, background): “ves (3.12) √ nIˆ∗ 1,n (y,x) ⇒ ∗ C∞ (y,x) where C∞ is the same Gaussian process of Theorem 1 and ⇒ ∗ denoting weak convergence in probability under the the bootstrap law (see Gin´e and Zinn (1990) and Ma and Kosorok (2005)). 16 4 Some extensions of the basic setup 4.1 Testing for Zero Conditional Ave”
Nonparametric identification in panels using quantiles (2013, background): “1See also Ma and Kosorok (2005) and Chen and Pouzo (2009, 2013) for other applications of weighted bootstrap; we are grateful to a referee for pointing out the latter references.”
CURE MODEL WITH CURRENT STATUS DATA (2009, methodology): “…nonparametric model in Groeneboom and Wellner (1992), the linear Cox model in Huang (1996), the additive risk model in Lin, Oakes and Ying (1998), the partly linear accelerated failure time (AFT) model in Xue, Lam and Li (2004), and the partly linear transformation model in Ma and Kosorok (2005a).”
Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals (2009, background): “In a recent paper Ma and Kosorok (2005) established a similar result for a semiparametricM-estimationwithout nonparametric endogeneity; we extend their results to the PSMD estimation of the conditional moment models (1.”
A Score Based Approach to Wild Bootstrap Inference (2012, unknown): “This approach is closer to the weighted bootstrap (e.g. Barbe and Bertail (1995), Ma and Kosorok (2005)) than the wild bootstrap and, in contrast to the score bootstrap, requires reoptimization of the estimator under each perturbation of the criterion function.”